RefereeBench shows that even the strongest video MLLMs reach only around 60% accuracy on multi-sport refereeing tasks and struggle with rule application and temporal grounding.
Breaking down video llm benchmarks: Knowledge, spatial perception, or true temporal understanding?
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
fields
cs.CV 4years
2026 4verdicts
UNVERDICTED 4representative citing papers
VideoABC estimates video-LLM failure probability via low-dimensional attribute projection, dual quantization (k-means plus lattice), and psychophysics-inspired synthetic data.
VISTA is a new ~12K-pair benchmark and taxonomy for open-set multi-entity spatio-temporal understanding in VLMs that decomposes videos into entities, actions, and relational dynamics for multi-axis diagnostics.
Video foundation models encode intuitive physics knowledge that is strongest in V-JEPA at intermediate-to-late layers and depends on pretraining type and probe design.
citing papers explorer
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RefereeBench: Are Video MLLMs Ready to be Multi-Sport Referees
RefereeBench shows that even the strongest video MLLMs reach only around 60% accuracy on multi-sport refereeing tasks and struggle with rule application and temporal grounding.
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An Attribute-Based Measure of Video Complexity
VideoABC estimates video-LLM failure probability via low-dimensional attribute projection, dual quantization (k-means plus lattice), and psychophysics-inspired synthetic data.
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VISTA: Video Interaction Spatio-Temporal Analysis Benchmark
VISTA is a new ~12K-pair benchmark and taxonomy for open-set multi-entity spatio-temporal understanding in VLMs that decomposes videos into entities, actions, and relational dynamics for multi-axis diagnostics.
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Do Video Foundation Models Understand Intuitive Physics? A Layerwise Probing Analysis
Video foundation models encode intuitive physics knowledge that is strongest in V-JEPA at intermediate-to-late layers and depends on pretraining type and probe design.